Healing Tables: When Day-by-Day Backfills Become a Slow-Motion Disaster
It was 2 AM on a Saturday when I realized we’d been loading data wrong for six months.
The situation: a customer dimension with three years of history needed to be backfilled after a source system migration. The previous team’s approach was straightforward—run the daily incremental process 1,095 times, once for each day of history. They estimated three weeks to complete.
What they hadn’t accounted for was how errors compound. By the time I looked at the data, we had 47,000 records with overlapping date ranges, 12,000 timeline gaps where customers seemed to vanish and reappear, and an unknowable number of missed changes from when source systems updated the same record multiple times in a single day.
